Meta · Open weights

Llama 3.3 70B Euryale

Added Jan 1, 2024Open weights

Use in the app

A 70B parameter model from SAO10K based on Llama 3.3 70B, offering high-quality text generation.

Input: Text
Output: Text
Discovery tier: Open weights

Pricing

USD per 1M tokens

Input: $0.49
Output: $0.49

Reference prices as published by the model provider. API example

Specifications

Context window: 20.5K
Max output: 16.4K
Average output (7 days): 173 tokens; Longer than 17% of 468 models

Side by side

Llama 3.3 70B Euryale compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

Limits
	Llama 3.3 70B EuryaleMetaThis model	WizardLM-2 8x22BmicrosoftRemove
Provider	Meta	microsoft
Discovery tier	Open weights	Open weights
Added	Jan 1, 2024	Apr 15, 2025
Context window	20.48K	65.54KBest
Max output	16.38KBest	8.19K
Average output	173 tokens	—
Pricing, USD per 1M tokens
Input	$0.49	$0.49
Output	$0.49	$0.49
Performance
Request success (24h)	15.8%	—
Capabilities
PDF input	No	Yes
Reasoning effort	—	—

Public evaluations

Benchmarks

There are no public benchmark results for these models yet.

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Request success (24h): 15.8%

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart

curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Sao10K/L3.3-70B-Euryale-v2.3",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'

import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="Sao10K/L3.3-70B-Euryale-v2.3",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)